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在使用卷积神经网络的磁共振成像中诊断多类脑恶性瘤

Junhui Lv¹, Liyang Wu², Chenyi Hong³

¹Department of Neurosurgery, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Qingchun Road, No. 3, Hangzhou, Zhejiang 310016, China.

Brain research bulletin

|April 3, 2025

概括

一个新的深度学习模型FoTNet使用MRI扫描精确区分质母细胞瘤 (GBM),初级中枢神经系统淋巴瘤 (PCNSL) 和大脑转移 (BM). 这种人工智能工具有助于精确,非侵入性诊断,以便更好地规划治疗.

科学领域:

神经瘤学神经瘤学
医疗成像医学成像
人工智能的人工智能

背景情况:

正确诊断恶性脑瘤,如质母细胞瘤 (GBM),初级中枢神经系统淋巴瘤 (PCNSL) 和大脑转移 (BM) 是具有挑战性的,因为类似的放射性特征.
非侵入性诊断方法对于指导适当的治疗策略至关重要.

研究的目的:

开发和验证一个深度学习模型,FoTNet,用于改进GBM,PCNSL和BM的自动诊断.
为了应对诊断PCNSL等较罕见瘤的挑战,PCNSL通常受到数据集中类不平衡的影响.

主要方法:

开发了一个深度学习模型,FoTNet,结合了基于频率的频道注意层和焦点损失来处理类不平衡.
采用了多中心数据集,其中包括来自58个GBM,82个PCNSL和269个BM病例的T1加权对比增强MRI图像.
数据集被分为训练和测试集,以5:2的比例进行模型评估.

主要成果:

在测试组中,FoTNet的分类准确率为92.5%,平均AUC为0.9754.
该模型在区分三种瘤类型方面明显优于现有的机器学习和深度学习方法.
多次验证证实了FoTNet在脑瘤分类中的有效性和稳定性.

结论:

福特网是用于准确,非侵入性地分类恶性脑瘤 (GBM,PCNSL,BM) 的高效和强大的工具.
该模型为手术前诊断提供了强有力的支持,帮助临床医生做出明智的治疗决策.

关键词:

脑部成像脑部成像深度学习是一种深度学习.磁共振成像技术磁共振成像技术在手术前进行诊断.瘤的分类瘤的分类

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Magnetic Resonance Imaging

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